Elastostatic fields in an anisotropic substrate due to a buried quantum dot
Bibliographic record
Abstract
We present an efficient and accurate continuum-mechanics approach for the numerical prediction of displacement, stress, strain, and strain energy density fields in an anisotropic substrate (modeled as a half-space) due to a buried quantum dot (QD). Our approach is based on Green’s function solution in anisotropic and linearly elastic half-space combined with the Betti’s reciprocal theorem. Numerical examples clearly show that the crystalline anisotropy of the III–V semiconductor group has great influence on the elastic fields, as compared to the isotropic solution. In particular, it is found that the hydrostatic strain and strain energy density on the surface of anisotropic half-space made of different crystalline materials due to a cubic QD can be substantially different, and therefore, the isotropy approximation neglecting their differences should not be used in general. Furthermore, the hydrostatic strains on the surface of an anisotropic half-space due to a finite-size (cubic) QD and an equal-intensity point QD at relatively large depth (about twice the side length of the cubic QD) can still be quite different, in contrast to the corresponding isotropic result. These observations indicate that in modeling and analyzing the mechanical and electronic behaviors of QD semiconductor structures, the effect of crystalline anisotropy should be considered in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".